Best Kafka UI tools for Google Managed Kafka Schema Registry

Tools compared

Rank	Tool	Total (out of 100)	Governance beyond IAM	Out of the data path	Google token sign-in	Decoding against the registry	Subjects and compatibility	Inspecting topic data	Cost a year, one cluster (modelled)
1	Kpow	86	RBAC, staged approvals, temporary access, masking, tenants, audit	One container, no external database, not a proxy	Google's bearer token provider, nothing stored	Avro and Protobuf in data inspect	Create, new version, compatibility, delete	kJQ search, data inspect, replay	$7,380
2	Google Cloud console and gcloud	66	IAM roles per principal, no approvals or masking	Nothing to deploy	Your own Google identity	No records	The native control plane, modes included	No records	$11,520
3	Redpanda Console	56	Behind a paid Enterprise licence	One container	A pasted token that expires	Undocumented on Google	Undocumented on Google	Message browsing	$8,640 plus an unpublished licence for RBAC
4	Kafbat UI	50	RBAC, global masking, opt-in audit	One container	OAuth2 client credentials, not Google's flow	None documented	None documented	Message browsing	$8,640
5	AKHQ	47	Group roles, no masking	One container	Custom image only	None documented	None documented	Message browsing	$8,640
6	Lenses	45	SSO and RBAC from Team tier, audit	HQ on PostgreSQL plus an agent per cluster	No Google setup documented	None documented	None documented	SQL over topics	$2,880 plus a quoted licence
7	Conduktor	53	Strong; data-level controls through Gateway	Console on PostgreSQL; Gateway, a proxy, for data-level controls	A pasted token that expires	Undocumented on Google	Undocumented on Google	Message browsing	$32,880; $122,880 with Gateway Core and Protect

The tools, ranked for Google Managed Kafka Schema Registry

What teams using Google Managed Kafka Schema Registry need

Google’s schema registry stores and version-checks the Avro and Protobuf contracts a team’s producers and consumers share, inside the same Google Cloud project as its Managed Service for Apache Kafka cluster. It does not show engineers the records those schemas describe, and it leaves the question of who may read or change what, once several teams share a tool, to IAM roles granted per principal. Everything on this page is about what a Kafka tool adds on top of the registry, not about replacing it. The cluster itself, its sign-in and Managed Kafka Connect are compared in the best Kafka UI tools for Google Cloud Managed Service for Apache Kafka, and registries of every kind in Kafka schema registry tools.

Two facts about the registry shape the rest of this page. It is still a Preview offering: every page of Google’s schema registry documentation, read on 3 October 2026, carries the Pre-GA banner, which says such features are available “as is” and might have limited support, and the predefined schema registry roles are marked Beta. And it is compatible with Confluent’s registry only up to a point. It implements the Confluent Schema Registry REST API, but Google’s list of limitations also rules out the metadata and ruleSet parameters when a version is created, and schema tags. Data contract rules that a Confluent registry stores beside a schema version therefore have nowhere to go on Google’s registry, so a team that moves from Confluent keeps those checks in its own pipelines or producers rather than in the registry.

Governance beyond IAM. IAM decides what each principal may do with the registry, and Google’s three predefined roles do not split along the line that matters most. The Schema Registry Editor role includes managedkafka.subjects.delete, managedkafka.versions.delete and even managedkafka.schemaRegistries.delete, yet it holds only managedkafka.config.get and managedkafka.mode.get, so an Editor can delete a subject or a whole registry but cannot change a subject’s compatibility type; of Google’s three schema registry roles, only Schema Registry Admin can. Kpow’s Google documentation recommends the Admin role for Kpow’s own service account, so the split between routine and destructive work has to happen in the tool. Kpow separates SCHEMA_CREATE, SCHEMA_EDIT_VERSION and SCHEMA_DELETE per person in its RBAC, and staged mutations hold a change for an admin’s approval before it runs.

The audit record splits the same way. Google’s audit logging reference classes every schema registry write, CreateVersion, DeleteSubject, DeleteVersion, UpdateSchemaConfig and UpdateSchemaMode among them, as an Admin Activity log, always written, under the identity that made the call. Reads, GetSchema, ListSubjects and ListVersions among them, are Data Access logs, which are off unless a team turns them on. When engineers work through a shared tool, the identity on every schema change is the tool’s service account, so Cloud Audit Logs show that a subject was deleted and not which engineer asked for it; the per-person record exists only in the tool’s own audit log, which records each action with the user from the identity provider. Deleting matters more than it looks. Google’s subject deletion guide offers a soft delete, which can be recovered, and a hard delete, which removes the subject permanently and in the console soft-deletes and then permanently deletes in one action. Per-person roles, an approval step before that action and an audit record of each engineer’s changes are what a shared tool has to add.

Out of the data path. Where the tool runs affects every requirement above. A tool that runs as a container in the team’s own VPC and connects to the brokers and the registry the way any client does adds nothing to the path producers and consumers take. A tool whose controls are enforced by a proxy that every client connects through becomes part of that path, and it has to be sized, kept available and kept in step with every client upgrade. Kpow is one container with no external database, installed in your own environment and out of the data path. Conduktor Console also connects directly, with a PostgreSQL database of its own; Conduktor’s data-level controls, such as encryption, masking of the data itself and virtual clusters, run in Conduktor Gateway, a Kafka proxy that client applications connect through.

The registry is not in the Kafka data path either, and it is not in the cluster’s VPC. Its endpoint is a Google API, https://managedkafka.googleapis.com/v1/projects/<project>/locations/<region>/schemaRegistries/<registry>, as Kpow’s example configuration shows, while the brokers sit behind Private Service Connect addresses in the VPC. A tool running on a VM or GKE node with only internal IP addresses therefore needs Private Google Access enabled on its subnet, or another route to Google APIs, before it can reach the registry; with it disabled, Google’s documentation says, such instances can only send traffic within the VPC network.

Google token sign-in. The registry accepts Google OAuth access tokens in the Authorization header and no username or password. Google’s token types reference says a service account access token expires after one hour by default, cannot be revoked and stays valid until it expires, and that a longer lifetime needs the iam.allowServiceAccountCredentialLifetimeExtension organisation policy. A tool that only accepts a fixed bearer token, as Redpanda Console and Conduktor Console do for schema registries, either stops working within the hour or holds a token stretched to 12 hours that nobody can revoke if the configuration leaks. Google’s Kafka auth library exists to avoid that: it authenticates with Application Default Credentials, which its README calls safer and simpler than using service account keys directly. Kpow loads that library’s GcpBearerAuthCredentialProvider, so tokens come from the service account attached to the VM or GKE workload. Because Confluent’s registry client instantiates every bearer provider it finds, Kpow ships Google support in a separate build while Google’s issue stays open, a cost that is also covered on the Google Cloud Managed Kafka page.

Decoding against the registry. Producers on Google’s registry use the standard Confluent serializers, which write a schema ID into each record and leave consumers to fetch the schema from the registry by that ID. Any tool that can sign in can therefore decode Avro and Protobuf records with the deserializers it already has; what separates the tools here is sign-in, not the wire format. JSON Schema is the exception. The registry API does not support JSON, so a team that already uses JSON Schema on another registry keeps those topics there or converts them, and a tool that attaches several registries to one cluster, as Kpow does with SCHEMA_REGISTRY_RESOURCE_IDS (Kpow schema registry configuration), can read both during the move. A record that still fails to decode is the usual starting point of a Kafka deserialization error.

Subjects and compatibility. The registry checks a new version against a compatibility type set for the registry, for a subject, or for a subject inside a named context, with BACKWARD as the default, and Google advises against NONE because schema changes can then break clients. Producers can change the contract themselves. In the workflow set out in Google’s schema registry overview, a producer can be configured to register a schema the registry does not yet hold under that subject, and receives the new ID; Google’s advice on that configuration is to avoid it in production environments. A team that follows that advice keeps registration with its deployment pipeline and routes changes made by people through a tool that records who made them.

Google adds a guard that a tool should respect: schema modes. A subject in READONLY mode accepts no new versions and no configuration changes, a subject’s own mode takes precedence over the registry’s, and a registry in READONLY mode blocks new subjects while a subject left in READWRITE can still change. Contexts let two teams use the same subject name in one registry without a conflict. The Google Cloud console manages all of these natively. Kpow lists each subject with its version and compatibility, and creates, edits and deletes subjects and changes compatibility from the same view; its Google documentation does not cover modes or contexts, which stay in the console or gcloud.

Inspecting topic data. The registry never shows a record. When a consumer reports a deserialization failure, the first job is to find the record and read it: which schema ID it carries, whether that version is still registered, and whether it was written by a serializer that used this registry at all. Google’s other route to topic data is a BigQuery sink connector, which suits questions about data already collected rather than one message during an incident.

No tool here leads on every criterion. The Google Cloud console is the native place to manage the registry, with modes and contexts included, and needs nothing deployed; Lenses has the strongest query model, without a documented way to reach Google’s registry. Kpow governs people working through Kpow, so applications keep their own service accounts, and IAM roles stay the control for them.

Kpow’s record on Google Managed Kafka Schema Registry

No Kpow customer has yet described in public how it runs Kpow with Google’s schema registry, so there are no customer cards on this page and the public record is Factor House’s own. Kpow added Google’s registry in release 94.3 in July 2025, as a variant of the Confluent-compatible registries it already supported. The step-by-step guide, Integrate Kpow with Google Managed Schema Registry, creates a registry, grants the Kafka and Schema Registry Admin roles to the client VM’s service account, registers an Avro schema from Kpow and reads the decoded records in data inspect. Since release 95.1 in December 2025, Google support ships in its own build, after a Factor House engineer raised the bearer provider issue with Google in November 2025.

How a team runs Kpow with Google Managed Kafka Schema Registry

Installing it beside the cluster. A team runs the GCP build, the temurin-ubi Docker tag, on GKE with the Helm charts or on a Compute Engine VM with Docker, in a subnet that reaches both the cluster and Google APIs. The cluster connection is covered in Set Up Kpow with Google Cloud Managed Service for Apache Kafka.

Connecting the registry. Kpow takes the registry’s URL in SCHEMA_REGISTRY_URL, Google’s provider class in SCHEMA_REGISTRY_BEARER_AUTH_CUSTOM_PROVIDER_CLASS and CUSTOM in SCHEMA_REGISTRY_BEARER_AUTH_CREDENTIALS_SOURCE, per the Google schema registry documentation. The workload’s service account holds the Managed Kafka Schema Registry Admin role, so no key or token goes into the configuration. Several registries, Google’s among them, can be attached to one Kafka cluster with SCHEMA_REGISTRY_RESOURCE_IDS.

Signing people in. Engineers sign in to Kpow through the company’s identity provider over SAML or OpenID Connect, or through LDAP, so access follows the company directory rather than the service account Kpow runs as.

Managing schemas. The schema view lists each subject with its type, version and compatibility. From there a permitted engineer creates a subject, edits it to register a new version, updates its compatibility or deletes it. RBAC separates SCHEMA_CREATE, SCHEMA_EDIT_VERSION and SCHEMA_DELETE, and staged mutations hold a deletion for an admin’s approval before it reaches the registry. Subjects a team wants frozen are set to READONLY in the Google Cloud console or with gcloud.

Reading Avro and Protobuf topics. Data inspect shows records decoded against the registry, and engineers filter them across topics with kJQ. Data policies mask sensitive fields in those results on the server.

Giving teams their own view. Tenants limit which topics, groups and schemas each role can see, and temporary policies grant production access that expires at a set time.

Keeping the record. The audit log records each schema change with the engineer from the identity provider, which is the name Cloud Audit Logs cannot show for work done through Kpow, and a webhook sends those records to Slack, Microsoft Teams or any endpoint.

The public Kpow demo needs no signup. It runs on Amazon MSK with AWS Glue, Karapace and Confluent registries rather than Google’s, and shows the same schema view and data inspect; Google sign-in, masking and temporary policies are the things to test in your own project.

Kpow live demo

See the Kpow schema view

The live Kpow demo runs on Amazon MSK with AWS Glue, Karapace and Confluent registries rather than Google's. Its Schema view shows the same subject list, versions and compatibility a team gets on Google's registry, with no signup.

For platform teams choosing a Kafka tool for Google Managed Kafka Schema Registry.

Try the Kpow demo→

FAQ

What is the best Kafka UI for Google Managed Kafka Schema Registry?

On this page’s rubric, Kpow, with 86 of 100 points: it connects with Google’s GcpBearerAuthCredentialProvider, decodes Avro and Protobuf records against the registry, creates, edits and deletes subjects and changes compatibility with each action a separate permission, adds per-person roles, masking, approvals and an audit trail on top of IAM, and runs as one container beside the cluster outside the data path. The Google Cloud console scores 66 as the registry’s own control plane, and Kpow Community Edition is free for up to 3 clusters and 10 users with Google’s registry included.

Which Kafka UI tools can connect to Google’s managed schema registry?

Kpow documents the connection with Google’s bearer token provider. Redpanda Console and Conduktor Console accept a fixed bearer token for a schema registry, which for a service account expires after one hour by default, and neither documents Google’s registry. Kafbat UI, AKHQ and Lenses document no route to it.

Does Google Managed Kafka Schema Registry support JSON Schema?

No. Google’s schema registry overview lists Avro and Protobuf and says the registry API does not support JSON. Topics that use JSON Schema stay on another registry, and Kpow can attach both registries to the same cluster.

Is Google Managed Kafka Schema Registry generally available?

Not when read on 3 October 2026. Its documentation pages carry Google’s Pre-GA banner for Preview features, and the predefined Schema Registry Admin, Editor and Viewer roles are marked Beta.

How do you audit who changed a schema in Google’s managed schema registry?

Cloud Audit Logs record each schema change, such as a new version, a deleted subject or a compatibility change, as an Admin Activity log under the identity that made the call. When engineers work through a shared tool, that identity is the tool’s service account, so Kpow’s audit log records each action with the user from the identity provider, and staged mutations can hold a change for an admin’s approval before it runs.

Which IAM role does a Kafka UI need on Google’s schema registry?

Of Google’s three predefined schema registry roles, Schema Registry Admin is the one that can change compatibility or schema modes, because the Editor role holds only read access to configuration and modes while it can still delete subjects, versions and registries. Kpow’s documentation recommends the Admin role for Kpow’s service account and Kpow’s own RBAC for what each person may do.

Can a Kafka UI reach Google’s schema registry from a private subnet?

Yes, once the subnet can reach Google APIs. The registry’s endpoint is on managedkafka.googleapis.com rather than in the cluster’s VPC, so a VM or GKE node with only internal IP addresses needs Private Google Access on its subnet.

Is there a free Kafka UI for Google Managed Kafka Schema Registry?

Kpow Community Edition is free on up to 3 clusters and 10 users and includes Google’s managed schema registry. Redpanda Console’s free build accepts a fixed bearer token, and Kafbat UI and AKHQ are open source without a documented route to the registry. RBAC, masking, staged approvals and the audit log need Kpow Enterprise. More free options are compared in the best free Kafka UI tools.

Does a Kafka UI need to sit in the data path to govern access to Google’s schema registry?

No. Kpow runs as one container in your project, calls the registry and the brokers like any client, and applies roles, masking, approvals and the audit trail to the people working through it, so producers and consumers keep calling the registry and the brokers directly. Conduktor Console also connects directly, while Conduktor’s data-level controls run in Gateway, a Kafka proxy that client applications connect through.

How these tools were scored

Three of the six criteria start from Google’s documentation for the schema registry; the other three are governance, where the tool runs and how it shows topic data, scored the same way as on Factor House’s other tool pages. They are listed here in order of weight. Each criterion is scored 0 to 10: 10 where a tool is the only one here doing it or clearly the best, 8 for a clean documented pass, 5 to 7 for partial support or support that needs work the reader must verify, 1 to 4 for a weak or indirect form, and 0 where it is absent.

1. Governance beyond IAM (counts three times). IAM decides what a principal may do with the registry and Cloud Audit Logs record each change under that principal. Neither gives a per-person view of the records a shared tool decodes, masking, or an approval step before a subject is hard-deleted, and when engineers share a tool the principal on record is the tool’s service account. This criterion scores per-person roles, production access granted on request and expiring, masking, directory sign-in, tenants for teams sharing a cluster, and an audit trail that names the person. Every option carries the same score as on the Google Cloud Managed Kafka page. The requirements for regulated teams are covered in Kafka governance tools for financial services.

2. Out of the data path (counts twice). The tool should run inside your own project and VPC, reach the brokers and the registry as an ordinary client, and keep no data outside your own cluster. Scored lower: tools that need an external database of their own, and tools whose controls work only when application traffic passes through a vendor’s proxy. A self-hosted container with no external database and no proxy scores 9, a tool with a database of its own 6, one with several databases or a component on the brokers 4, and one that needs both a database and a proxy for its controls 3; 10 is kept for an option with nothing to deploy at all, here the Google Cloud console. This criterion is scored the same way on every Factor House page that uses it, and only its weight changes with the reader.

3. Google token sign-in (counts twice). The registry accepts Google access tokens only. A tool scores 10 when it uses each engineer’s own Google identity, 8 when it documents fetching tokens from the workload’s Google credentials with nothing stored, 3 when it accepts only a fixed bearer token that a team has to paste in and replace, 2 when nothing is documented and the team works out the flow itself, whether through a custom image, an OAuth flow that does not match Google’s, or settings a vendor has announced but not shipped.

4. Decoding against the registry (counts once). Producers use the standard Confluent serializers, so a tool that can sign in can decode Avro and Protobuf records. A tool scores 8 when decoding against Google’s registry is documented, 3 when it would work only for as long as a pasted token lasts, 1 when no route is documented, and 0 when the option never reads records.

5. Subjects and compatibility (counts once). Creating a subject, registering a version, changing a compatibility type and deleting are the changes that break consumers when they go wrong. The Google Cloud console, as the registry’s own control plane with modes included, scores 10; a tool that documents all four on Google’s registry scores 8; one whose schema view would work only with a pasted token scores 3; one with no documented connection scores 1.

6. Inspecting topic data (counts once). Reading a message, searching a topic for one key or replaying a record after an incident is the day-to-day job engineers need a tool for. It is scored the same way as on the Google Cloud Managed Kafka page.

Costs are modelled for one production cluster and 25 engineers at $120 per engineer hour, using the same hours per tool class as Factor House’s other comparison pages. Tools with a licence carry the published price plus 2 hours a month to run. The open-source UIs carry 6 hours a month, $8,640 a year, to run, secure and keep current. The Google Cloud console has no licence and nothing to run, and the Kafka command-line tools it leaves record reading to are modelled like the other tools with no access control of their own, at 8 hours a month, $11,520 a year. Kpow’s $7,380 uses its price of $4,500 per cluster a year with 100 users included. Kpow Community Edition is free for 3 clusters and 10 users, so a 25-engineer team is on Enterprise. Google’s own charges for the cluster are the same whichever tool a team chooses, so they are left out.

The criteria map onto the registry’s features in the figure below.

F1 From Google schema registry feature to what a Kafka tool has to do

What Google's schema registry does What the tool has to do

Sign-in Accepts Google OAuth access tokens, which for a service account expire after one hour by default Fetch fresh tokens from the workload's own credentials, rather than hold a pasted token or a key

Formats Avro and Protobuf; the registry API does not support JSON Decode Avro and Protobuf records against the registry, and say plainly that JSON Schema is not on offer

API edges Implements the Confluent Schema Registry REST API, without ListSchemas, data contract rules or tags Work through the calls Google implements, and not depend on the ones it leaves out

IAM roles Admin, Editor and Viewer roles, in Beta; Editor can delete subjects but not change compatibility Split creating, editing and deleting per person, since one service account holds one role

Audit Schema changes are Admin Activity logs under the caller; schema reads are Data Access logs, off by default Record which engineer made each change, because through a shared tool the caller is the tool

Modes Readonly or Readwrite, per registry or per subject Show a subject's mode, and keep a change behind approval when a subject is left writable

Where the tool runs The registry is a Google API at managedkafka.googleapis.com, outside the cluster's VPC Run beside the cluster as an ordinary client, with a route to Google APIs, not as a proxy that applications route through

Every option is scored from 0 to 10 on each criterion, from the evidence and sources this page cites, and the reason for each score is on its card. The criteria are weighted: Governance beyond IAM counts three times, Out of the data path counts twice, Google token sign-in counts twice, Decoding against the registry counts once, Subjects and compatibility counts once and Inspecting topic data counts once, for a total out of 100. Governance beyond IAM counts three times because Google's registry authorises the principal that calls it, and through a shared tool that principal is the tool's service account, so per-person roles, production access granted on request, masking, directory sign-in, tenants for shared clusters and a per-person audit trail are the only record of which engineer read or changed what. Out of the data path and Google token sign-in count twice: a tool that clients connect through becomes part of the path every producer and consumer depends on, and one that needs a database of its own is one more component to run and secure, while a tool that cannot fetch its own Google tokens ends up holding a pasted token that expires or a service account key. Decoding against the registry, subjects and compatibility, and inspecting topic data count once. This page is published by Factor House, which makes Kpow. Every option is scored on the same rubric and the same sources: Kpow's per-criterion scores are set the same way as every other option's and are not adjusted, and the weights apply to every option alike. Kpow ranks first on its total of 86 out of 100. The other options follow by total. Conduktor is listed last whatever its total; on its total of 53 it would place fourth.

Related reading

• Kafka: the complete guide
• Best Kafka UI tools for Google Cloud Managed Service for Apache Kafka
• Best Kafka UI tools for Confluent Schema Registry
• Best Kafka UI tools for AWS Glue Schema Registry
• Kafka schema registry tools
• Integrate Kpow with Google Managed Schema Registry
• Set Up Kpow with Google Cloud Managed Service for Apache Kafka
• Best Kafka governance tools for financial services